Although DL applications have the natural advantage of scalability [1]} for lack of a bottleneck like the FL global aggregation server [2]}, the absence of a central server and global model make it hard to mitigate data heterogeneity. For instance, with the knowledge of global gradient (i.e. by averaging all local gradients), FL servers are ideally positioned for computing the global gradient direction and relative local gradient drifts [3]} providing them a better means of addressing data heterogeneity. Replicating drift computation in DL requires knowledge of all local gradients by all nodes. I.e. a fully connected p2p network (complete graph of peers). Despite the optimal convergence of a fully connected DL graph [4]} however, such a dense topology is infeasible as it incurs a large communication overhead especially as the system scales.
